Concepts of Machine Learning

Module outline

Using a combination of lectures and lab work, the module covers popular machine learning paradigms giving students knowledge of advanced features of various methods at the theoretical and practical levels.

Aims

The module covers computational algorithms for learning from data, data-driven decision making and complex problem solving. It provides an introduction to machine learning methods, such as neural networks, fuzzy logic, fuzzy clustering, natural computing, and covers basic concepts of feature selection and generalisation.

Prerequisites

No specific module is pre- or co- requisite but knowledge of mathematical concepts (algebraic concepts, vector, matrix, function and graph, gradient, trigonometry concepts, statistical concepts and the notion of probability) and data structures and algorithms is essential.

Timetable

All dates and timetables are listed in the programme handbooks of individual programmes.